fig = plt.figure()
a = np.array([-gpgo._acqWrapper(np.atleast_1d(x))
for x in x_test]).flatten()
r = fig.add_subplot(1, 1, 1)
r.set_title('Acquisition function')
plt.plot(x_test, a, color='green')
gpgo._optimizeAcq(method='L-BFGS-B', n_start=25)
plt.axvline(x=gpgo.best, color='black', label='Found optima')
plt.legend(loc=0)
plt.tight_layout()
plt.show()
if __name__ == '__main__':
np.random.seed(321)
def f(x):
return (np.sin(x))
sexp = squaredExponential()
gp = GaussianProcessMCMC(sexp, step=pm.Slice)
acq = Acquisition(mode='IntegratedExpectedImprovement')
param = {'x': ('cont', [0, 2 * np.pi])}
gpgo = GPGO(gp, acq, f, param, n_jobs=-1)
gpgo._firstRun()
for i in range(6):
plotGPGO(gpgo, param)
gpgo.updateGP()
# Load binary truths
binary_truth = np.loadtxt(
"./data/" + lang + "/semeval2020_ulscd_" + lang[:3] +
"/truth/binary.txt",
dtype=str,
delimiter="\t",
)
# Creating a GP surrogate model with a Squared Exponantial
# covariance function, aka kernel
sexp = squaredExponential()
sur_model = GaussianProcess(sexp)
fitness = get_fitness_for_automl(model1, model2, binary_truth, logger)
# setting the acquisition function
acq = Acquisition(mode="ExpectedImprovement")
# creating an object Bayesian Optimization
bo = GPGO(sur_model, acq, fitness, param, n_jobs=4)
bo._firstRun = functools.partial(myFirstRun, bo)
bo.updateGP = functools.partial(myUpdateGP, bo)
bo._firstRun(init_rand_configs=init_rand_configs)
bo.logger._printInit(bo)
bo.run(furtherEvaluations, resume=True)
best = bo.getResult()
logger.info("BEST PARAMETERS: " +
", ".join([k + ": " + str(v)
for k, v in best[0].items()]) + ", ACCU: " +
str(best[1]))
logger.info("OPTIMIZATION HISTORY")
logger.info(pprint.pformat(bo.history))
from pyGPGO.acquisition import Acquisition
from pyGPGO.covfunc import squaredExponential
from pyGPGO.GPGO import GPGO
if __name__ == '__main__':
sexp = squaredExponential()
gp = tStudentProcessMCMC(sexp, step=pm.Slice)
def f(x):
return np.sin(x)
np.random.seed(200)
param = {'x': ('cont', [0, 6])}
acq = Acquisition(mode='IntegratedExpectedImprovement')
gpgo = GPGO(gp, acq, f, param)
gpgo._firstRun(n_eval=7)
plt.figure()
plt.subplot(2, 1, 1)
Z = np.linspace(0, 6, 100)[:, None]
post_mean, post_var = gpgo.GP.predict(Z, return_std=True, nsamples=200)
for i in range(200):
plt.plot(Z.flatten(), post_mean[i], linewidth=0.4)
plt.plot(gpgo.GP.X.flatten(),
gpgo.GP.y,
'X',
label='Sampled data',
markersize=10,
color='red')
if __name__ == '__main__':
def f(x):
return (np.sin(x))
acq_1 = Acquisition(mode='ExpectedImprovement')
acq_2 = Acquisition(mode='ProbabilityImprovement')
acq_3 = Acquisition(mode='UCB', beta=0.5)
acq_4 = Acquisition(mode='UCB', beta=1.5)
acq_list = [acq_1, acq_2, acq_3, acq_4]
sexp = squaredExponential()
param = {'x': ('cont', [0, 2 * np.pi])}
new = True
colors = ['green', 'red', 'orange', 'black']
acq_titles = [
r'Expected improvement', r'Probability of Improvement',
r'GP-UCB $\beta = .5$', r'GP-UCB $\beta = 1.5$'
]
for index, acq in enumerate(acq_list):
np.random.seed(200)
gp = GaussianProcess(sexp)
gpgo = GPGO(gp, acq, f, param)
gpgo._firstRun(n_eval=3)
plotGPGO(gpgo, param, index=index + 2, new=new)
new = False
plt.show()
